ArticleAnalytica chimica acta2025
Spatial atlasing of N-glycosylation in healthy control and clear cell renal cell carcinoma tissues linked with immune checkpoint inhibition response by MALDI mass spectrometry imaging.
Article in Analytica chimica acta, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundClear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer. While most cases are detected early and treated by surgery, a relevant proportion of patients present with metastases and require medical treatment. Immune checkpoint inhibition (ICI) has become a key component of treatment for metastatic ccRCC patients and may produce complete responses in a fraction of patients. Presently, there is no predictive biomarker for response to ICI which could guide individualized treatments, enrich response rates and minimize unnecessary treatment in patients unlikely to benefit from ICI.
resultsIn this study, we employed Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging to characterize the N-glycome of healthy control kidneys and 21 treatment naïve, primary RCC tumor samples. Patients were treated with ICI following tumor resection and stratified by response. Two candidate N-glycans were found to be upregulated in the group that experienced longer progression free survival and exhibited a response to PD-1/PD-L1 checkpoint inhibition. Additionally, we established N-glycosylation patterns which correspond to renal microanatomic structures including proximal convoluted tubules, distal convoluted tubules, and glomeruli. To more fully evaluate the tumor microenvironment of the different immunotherapy responder groups, we also conducted innovative immunohistochemical analyses visualized via mass spectrometry. SIGNIFICANCE: This study represents an expanded spatial characterization of the normal renal N-glycome. Additionally, the N-glycomes of ccRCC tissues were compared relative to normal tissues, and in context of response to ICI. Candidate glycan-based biomarkers could eventually be developed for clinical translational applications at baseline to predictively identify responders and non-responders to ICI.
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